Using co-occurrence network structure to extract synonymous gene and protein names from MEDLINE abstracts.
Using co-occurrence network structure to extract synonymous gene and protein names from MEDLINE abstracts.
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DOI：
10.1186/1471-2105-6-103
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发表时间：
2005-04-22
影响因子：
3
通讯作者：
Spackman K
中科院分区：
文献类型：
--
作者：
Cohen AM;Hersh WR;Dubay C;Spackman K
文本挖掘可以帮助生物医学研究人员从大量的文本中提取有用的知识，从而减少信息过载。我们开发了一种新的文本挖掘方法的基础上分析的网络结构创建的符号同现作为一种方式来扩展知识提取的能力。将该方法应用于基因和蛋白质名称同义词自动提取。性能是在一个测试集上测量的，该测试集由来自MEDLINE一年的约50，000篇摘要组成。从策划的基因组学数据库检索的同义词被用作金标准。该系统获得了22.21%的最大F值（23.18%的准确率和21.36%的召回率），在种子对的使用效率高。该方法与其他研究方法进行了比较，不依赖于复杂的命名实体识别，并且需要很少的初始种子知识。
Text-mining can assist biomedical researchers in reducing information overload by extracting useful knowledge from large collections of text. We developed a novel text-mining method based on analyzing the network structure created by symbol co-occurrences as a way to extend the capabilities of knowledge extraction. The method was applied to the task of automatic gene and protein name synonym extraction. Performance was measured on a test set consisting of about 50,000 abstracts from one year of MEDLINE. Synonyms retrieved from curated genomics databases were used as a gold standard. The system obtained a maximum F-score of 22.21% (23.18% precision and 21.36% recall), with high efficiency in the use of seed pairs. The method performs comparably with other studied methods, does not rely on sophisticated named-entity recognition, and requires little initial seed knowledge.
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影响因子：
5.3
作者：
Povey, S;Lovering, R;Wain, H
通讯作者：
Wain, H
影响因子：
5.8
作者：
Kolpakov, FA;Ananko, EA;Kolchanov, NA
通讯作者：
Kolchanov, NA
影响因子：
4.5
作者：
Hirschman, L;Morgan, AA;Yeh, AS
通讯作者：
Yeh, AS
影响因子：
14.9
作者：
Gelbart, W;Bayraktaroglu, L;Wiel, C
通讯作者：
Wiel, C
影响因子：
14.9
作者：
Kanehisa, M;Goto, S
通讯作者：
Goto, S